The body remains, but the self doesn’t.

We understand something in those moments that we rarely say plainly. And perhaps, it’s time we put this idea front and center. Cognition isn’t merely something a person has, it’s something a person is.

Day after day, we become ourselves through the act of thinking. From the complex to the trivial, we traverse a reality that bumps and bruises us into personhood. And that friction isn’t an obstacle to identity, it’s how identity forms.

Aristotle understood this long before neuroscience provided a name for it. Character isn’t something we possess. It is something we create. What we think shapes what we do. What we do, repeatedly, shapes who we become. Which is why the question of artificial intelligence, at least to me, isn’t primarily a question about productivity or efficiency.

Of course, AI doesn’t arrive as a threat, it arrives as a relief. And that’s what makes it so insidious. There’s no cognitive check engine light to warn you. There’s just the comfort of a swift and almost effortless answer. The friction that used to shape you simply didn’t happen. Do that enough times and something changes, not dramatically, but in the way that habits shift things. Gradually, then all at once.

Technology has always extended human capability. The wheel extended our legs. Writing extended memory. The calculator extended arithmetic. But AI is different in kind, and not merely degree. It reaches into cognition itself, into the territory where “we” live—into the domain of judgment, understanding, and idenity. A calculator doesn’t threaten to do your becoming for you.

The neuroscientist Michael Merzenich is well-known for the mechanism that we today call neuroplasticity. Simply put, neural connections are strengthen when used and weakened when not. The brain adapts continuously to the demands placed upon it. This isn’t a lofty metaphor but measurable biology. The brain you exercise is not the brain you don’t.

But cognitive surrender isn’t a neutral act. Every decision handed off to AI are small withdrawals from the account of the self. Of course, handing the process over to a machine provides certain efficiency or even relief, but you step away from the mechanism through which you author, well, you.

There is a phrase, adapted from the Upanishads, that I alluded to earlier: as you think, so you act. As you act, so you become. This is doing more than describing habit. It is describing identity formation. We are not simply what we know. We are, in part, what we have struggled to understand.

The answers may still sound like you. What fills the space is not.

That’s the abyss. Not a dramatic fall, but a quiet retreat from the very process that makes a person a person.

I wrote about the Borrowed Mind as a possibility. Now, I think it’s worth asking, with some regularity, whether it has become a habit.

John Nosta is the author of the best seller:  The Borrow Mind—Reclaiming Human Thought in the Age of AI.

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The Exhibition Floor as a Market Signal

However, after several days walking the exhibition floor and listening to discussions across multiple stages, another story emerged. The most interesting development at HLTH Europe was not the continued rise of AI. It was the growing number of companies applying artificial intelligence to solve very specific challenges faced by researchers, physicians, health systems and patients.

What appears on the stages and exhibition floor at HLTH often reflects where the market sees opportunity. Conferences do not create trends. They reveal them. HLTH Europe brought together more than 400 speakers, some 350 sponsors and approximately 5,000 participants from across the global health ecosystem. Artificial intelligence was not simply one topic among many. The conference featured a dedicated AI @ HLTH Zone, AI-focused exhibitors and numerous sessions exploring implementation, governance, clinical applications and operational adoption.

The prominence of AI across both the agenda and exhibition hall was revealing. Conference organizers dedicate space and programming to topics that matter to attendees, investors and sponsors. The visibility of AI at HLTH Europe suggested that health-specific applications of artificial intelligence have moved beyond emerging interest and are now a significant market focus.

That shift matters because health has always demanded more than technological capability. New tools must operate within environments where privacy, safety, accountability and trust are essential. Researchers are looking for ways to accelerate discovery. Physicians want to reduce administrative burdens that consume valuable time. Health systems seek efficiencies that improve operations without compromising quality. Increasingly, innovators are designing AI solutions around those specific needs.

That reality helps explain why many of the most compelling AI companies at HLTH Europe are building solutions specifically for health rather than adapting tools designed for other industries.

As Sophie Taylor-Roberts, managing partner and FINN Partners UK Health Group Lead, shared: “A mistake in healthcare carries a human cost: it can literally mean life or death. That’s why healthcare needs bespoke AI models, tools and solutions that allow for diverse patient populations, differing clinical guidelines, funding and regulatory structures.”

She added, “As with all aspects of health, one size doesn’t fit all. AI must be treated like a highly specialized medical instrument, built to respect national sovereignty, multilingual patient care, and absolute data privacy.”

Health-Specific AI Moves from Possibility to Practice

The trend was visible throughout the exhibition hall, where companies focused on clinical research, physician workflow, diagnostics, patient engagement, digital safety and operational efficiency demonstrated how specialized AI is rapidly becoming a category of its own.

The trend was visible throughout the exhibition hall, where companies focused on clinical research, physician workflow, diagnostics, patient engagement, digital safety and operational efficiency demonstrated how specialized AI is rapidly becoming a category of its own. Their growth reflects a broader shift occurring across the health sector as organizations seek tools designed for specific scientific, clinical and operational challenges.

Gabriele Ricci, Chief Data & Technology Officer at Takeda, captured that evolution when discussing AI’s growing role across the research and development continuum. “AI is transforming the future of healthcare by accelerating every stage of the R&D value chain through purpose-built capabilities tailored to specific scientific and clinical challenges,” he said.

His emphasis on purpose-built capabilities mirrors what was visible throughout HLTH Europe. The conversation is no longer centered exclusively on artificial intelligence as a technology platform. Increasingly, attention is turning toward how specialized applications can address distinct needs across research, clinical care and health operations.

Among the companies reflecting this shift was Briya, whose AI-powered platform helps researchers interact with complex data through conversational interfaces. Rather than requiring users to navigate multiple databases, coding environments and analytical tools, the platform seeks to simplify the path from question to insight.

David Lazerson, Briya’s co-founder and chief executive officer, believes many organizations misunderstand where the greatest challenge in AI adoption resides.

“Many people assume AI adoption is about choosing the right model,” he said. “In reality, the model is only a small part of the solution. The hard part is everything around it: security, governance, data harmonization, domain expertise, and the methodology required to produce trustworthy outcomes.”

His observation reflects a reality becoming increasingly evident throughout the health sector. Access to powerful AI models is expanding rapidly, shifting competitive advantage toward organizations that can generate reliable outcomes within specific health environments. That reality helps explain the growing number of exhibitors focused on narrowly defined use cases rather than general-purpose AI.

A similar perspective emerged from conversations with Keith Grimes, MD, Chief Innovation Officer at Curistica. A physician who spent 24 years in primary care, Grimes approaches artificial intelligence through the lens of risk management, governance and patient safety.

“Physicians have always governed risk,” he explained. “We do it instinctively for doctors, drugs and devices. Digital is just the fourth D, and the discipline is much the same, but it is the one we were never trained for, so the commitment to ‘do no harm’ runs ahead of the know-how.”

His comments address one of the most significant challenges facing health organizations today. Many leaders recognize the promise of AI, yet remain uncertain about implementation, oversight and accountability, particularly in smaller physician practices and community-based care settings.

Dr. Grimes emphasizes that smaller organizations should not view those limitations as barriers.

“Small practices are the cornerstone of primary care, but they cannot out-resource a hospital trust, and it does not need to,” he said. “Good governance scales down, and the same standards that protect a large organization can be borrowed rather than rebuilt.”

“We give whoever is responsible for AI and digital safety both the platform and the people,” Dr. Grimes said. “Power tools that guide them, whatever their experience, with clinical safety experts behind the software.”

Taken together, the perspectives of Dr. Grimes and Lazerson point to the emergence of a new category of innovation. The most promising health AI companies are not focused exclusively on algorithms. They are creating environments that combine technology, expertise and governance to solve specific high-friction problems.

The Future Belongs to Reliable Outcomes

For smaller organizations, this evolution may prove particularly significant. Historically, adopting advanced technology often required substantial investment, specialized technical talent and complex integration efforts. Many health organizations lacked the resources to pursue those initiatives.

Lazerson believes that model is changing. “That’s why we’re seeing the emergence of a new layer of domain-specific AI,” he said. “Instead of every organization hiring AI engineers and building custom infrastructure, they can access a complete, purpose-built environment as a service.”

The implications extend far beyond research organizations. Physician practices, community health providers, home health agencies and emerging life science companies increasingly have access to capabilities that previously required significant internal resources.

“For smaller organizations in particular, it’s a no-brainer,” Lazerson added. “They can start generating value immediately without complex integrations, dedicated AI teams, or having to solve privacy, security, and compliance challenges on their own.”

Throughout HLTH Europe, companies focused on clinical research, workflow automation, diagnostics, care coordination and patient engagement demonstrated how artificial intelligence is becoming increasingly specialized. Rather than attempting to transform every aspect of health simultaneously, they are concentrating on areas where measurable value can be achieved quickly and responsibly.

That focus on practical outcomes may ultimately become the defining characteristic of the next generation of health innovation.

Dr. Grimes summarized the principle succinctly. “Safety is not a box-ticking exercise; it works when everyone knows the part they play,” he said. “The advantage is not scale, it is fit.”

Walking through HLTH Europe, I was reminded that innovation rarely advances through a single breakthrough. More often, progress emerges through focused efforts to solve meaningful problems. The companies attracting attention were helping researchers move faster, supporting clinicians facing administrative burdens and enabling organizations to adopt new capabilities with greater confidence.

Perhaps among the more important lessons from HLTH Europe. The future of AI in health will not be defined solely by the largest platforms. It will be shaped by innovators who combine technology, expertise, and specificity to deliver reliable outcomes. As Lazerson observed, “The future won’t belong to organizations with the biggest models. It will belong to those who can turn AI into reliable outcomes.”

Judging by what appeared across the stages and exhibition floor in Amsterdam, that future is taking shape.

The post At HLTH Europe, the Most Important AI Story Was Happening Beyond the Headlines appeared first on Medika Life.

Despite living longer than men, women spend approximately 25 percent more of their lives in poor health, according to research from the World Economic Forum and the McKinsey Health Institute. Across reproduction, brain health, autoimmune conditions, cardiovascular disease, and mental health, the gaps in research, funding, and care are persistent.

That reality provided important context for the launch of The Shift, a new mini documentary series from BBC StoryWorks Commercial Productions, unveiled at HLTH Europe. The series explores issues ranging from reproductive health and cardiovascular disease to autoimmune disorders, menopause, mental health and healthy aging. Through storytelling, the documentary project elevates the experiences of women while highlighting the challenges that persist and the opportunities for progress.

The HLTH EU panel discussion was timed for the opening of The Shift and featured Shahnoor Abbas, Senior Series Developer and Research Development Lead for The Shift at BBC StoryWorks Commercial Productions; Elena Bonfiglioli, General Manager, Global Health & Life Sciences at Microsoft, and Priya Agrawal, MD, Vice President, Global Health Equity and Partnerships at MSD. Their conversation, moderated by Jody Tropeano Greene, Head of Content for HLTH, explored why women’s health remains one of the most significant opportunities for innovation, investment and system improvement.

A Conversation Decades in the Making

The panelists approached the topic from different perspectives, yet a common theme emerged. Women’s health has received increasing attention for more than a decade, but many of the barriers women encounter remain rooted in the design of health systems.

For BBC StoryWorks, The Shift represents an effort to sustain attention on issues that too often receive episodic interest. The series combines personal stories with broader insights into the realities women face across different countries, cultures and stages of life.

The BBC initiative and the HLTH EU mainstage conversation arrive at a time when women’s health is attracting growing attention from investors, entrepreneurs, policymakers and health industry leaders. New companies are emerging. New technologies are being developed. More organizations are recognizing both the societal and economic importance of addressing longstanding gaps in care.

The timing of The Shift is notable. Women’s health innovation is receiving growing attention from investors, entrepreneurs, policymakers and health leaders. Industry analysts estimate that approximately $2 billion was invested in venture-backed women’s health companies across the United States and Europe in 2025, reflecting increased interest in addressing challenges that extend beyond reproductive health to include cardiovascular disease, menopause, mental health, oncology and healthy aging.

The trend signals growing recognition that improving women’s health is a societal imperative and a significant economic opportunity. Yet as the discussion at HLTH Europe made clear, investment and innovation alone will not be enough if women continue to face fragmented systems that are difficult to navigate.

When Access Exists but Navigation Fails

Dr. Agrawal, an obstetrician-gynecologist by training, whose work has included clinical practice in the UK NHS, global pharma brand stewardship in emerging middle-income nations, maternal health awareness initiatives, and the creation of sustainable health markets, described a reality familiar to many women. Access to care may exist on paper; however, reaching that care, understanding available options and navigating fragmented systems remains a challenge.

“We’ve built systems like mazes with different entry points, different providers and different messages,” said Dr. Agrawal. “Women are often left navigating all of this themselves at the moments where they are most vulnerable.”

Her observation echoed the comments by fellow panelists, which touched on an issue that extends beyond women’s health. Across many countries, patients frequently encounter disconnected providers, inconsistent communication and care journeys that require them to coordinate appointments, referrals and information on their own. The burden of connecting those pieces often falls on the individual seeking care rather than the system intended to support them.

For women, that complexity can be especially challenging. Responsibilities related to caregiving, work, family and personal health often intersect at the very moment care is needed. Understanding what symptoms are normal, knowing when to seek help, determining where to go and finding trusted sources of information become added obstacles.

That reality led to one of the discussion’s compelling observations. “This is not an access problem. It’s a design problem.”

The distinction matters. Discussions about women’s health often focus on whether services exist. Design asks a different question: can people realistically find, understand and benefit from those services when they need them most?

The Power of Stories to Sustain Change

The panel also explored the role technology may play in addressing those challenges. Rather than adding new layers of complexity, emerging digital tools and artificial intelligence applications are increasingly being developed to simplify navigation, improve continuity and support people between clinical encounters.

“What excites me is that technology is finally starting to reduce friction instead of adding layers of complexity,” Dr. Agrawal observed.

That perspective aligned with comments from Bonfiglioli, whose work at Microsoft focuses on helping health systems leverage data, cloud technologies and artificial intelligence to improve outcomes. Technology, however, was not presented as a solution on its own. The discussion repeatedly returned to the importance of human connection.

Those themes are central to the documentary series itself. BBC StoryWorks has built a reputation for transforming complex issues into compelling narratives that audiences can understand and relate to. Through The Shift, the goal is not merely to document challenges but to foster greater understanding of the experiences women face and the opportunities that exist to improve care.

Abbas emphasized the power of storytelling to connect data and lived experience. Statistics can identify a problem. Research can explain it. Stories help people understand why it matters and why action is necessary.

That may be the enduring value of The Shift. The series does not introduce a new conversation. Instead, it brings fresh perspectives to longstanding challenges. Through stories from around the world, the films remind viewers that behind every statistic is a person navigating the complexities of health and care. By fostering greater understanding and empathy, the series encourages health leaders, innovators and policymakers to view women’s health not as a periodic topic of interest, but as an ongoing priority deserving sustained attention and action.

The women featured throughout the series deserve more. The discussion at HLTH Europe reinforces that improving women’s health is more than developing new technologies and expanding services. It is also about creating systems that are easier to navigate, more responsive to people’s medical priorities and ultimately more human in their design.

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What followed feels painfully familiar. Factory owners and industry leaders warned that stronger safety standards, sprinklers, unlocked exits, occupancy limits, and reasonable working hours would cripple American business. Human protection, they argued, was too expensive for the economy to bear.

They were wrong. The tragedy gave rise to the Factory Investigating Commission, the tipping point that led to more than 30 new laws and helped shape the modern American workplace. Today, no serious business leader would argue that unlocked exits, fire escape or basic worker protections weaken the economy. What industry once framed as an unbearable burden became the foundation of responsible business, proving that human safety and long-term productivity, stability, retention, and reputation are inseparable.

We are standing in a similar moment now, and once again, too many leaders are mistaking short-term cost for long-term economic sustainability — the perception of survival.

Today’s equivalent is the belief that purpose, a company’s responsibility to its employees, communities, and environment, is little more than affinity branding. A few lines in the annual report. A polished message for the all-hands staff meeting. Something ornamental rather than operational.

It is the same failure of vision that defined many factory owners in 1911. They saw worker protection as an expense instead of the foundation of a sustainable enterprise. Too many companies still treat sustainability and social responsibility as peripheral to performance, when in reality they are becoming inseparable from resilience, talent retention, operational continuity and customer advocacy. The evidence is growing that this is not simply an ethical argument; it is a business one.

The case also has nothing to do with operating in a “do-good” sector. The 1911 reforms applied to garment factories, foundries and machine shops. Purpose belongs as much to a regional bank, a logistics company, a chip manufacturer, or an ad agency as it does to a B Corp or a clean-energy startup. Every sector employs people, draws on a community, and depends on social and natural systems it did not build, whether or not it markets itself that way.

Consider one number. Gallup’s 2025 State of the Global Workplace report estimates that disengagement costs the world economy roughly $10 trillion in lost productivity each year, a gap equivalent to about 9% of global GDP, while companies with strong recognition and development practices see up to 21% higher profitability. Employee well-being is a direct input to performance on a longer time horizon than the next quarter.

The community piece follows the same logic, and the AI data center boom is the freshest illustration. Residents near new data centers in rural Georgia have reported depleted or contaminated water supplies, and roughly two-thirds of data centers built since 2022 sit in water-stressed regions.

These projects bring construction jobs and tax revenue, but the externalities often fall on the people next door. What happens to their water bills when the aquifer drops? To their home equity when a 200-acre concrete box appears across the road? Or, to the residential electricity rates raised to fund grid upgrades that the data center triggered?

The harder question is how to build in a way that makes the host community better off than before. Operators investing in closed-loop cooling, local water infrastructure, grid upgrades, and tax agreements that share rather than extract value are building a license to operate that will outlast any capex cycle. Over time, brand reputation becomes the cumulative memory of how a company treated the people around it.

Environmental responsibility is the clearest case of all, because the economic vocabulary has finally caught up. Climate exposure is a supply chain risk. Water stress is an operational risk. Biodiversity loss is an input risk. Emissions pose regulatory and capital-cost risks. Companies that dismiss decarbonization as peripheral may be making a similar strategic mistake. Firms that grasped earlier that resilience underwrites profit will outcompete them.

The link between 1911 and 2026 is not philosophical. It is operational. In every era, the responsibilities of business expand to match what we have learned about how value is actually produced, perceived and sustained. Policymakers may argue that expanding employee benefits will ruin the economy. Instead, the economy becomes stronger, employees have greater protection and are more engaged, and the companies that adapt often pull ahead.

A century from now, no one will look back at the companies that invested in their employees’ welfare, treated host communities as partners, and prioritized decarbonization. Few, if any, will call investing in those stakeholder connections a step backward. They will call it what we now call unlocking factory doors: progress, overdue and obvious steps in cementing connections.

In retrospect, societies rarely regret the moments when business broadened its sense of responsibility. We now see protections once dismissed as burdens, like unlocked factory doors and fire escapes, as obvious expressions of human dignity and common sense. Once embraced, it becomes difficult to imagine a world without them.

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A growing body of research is connecting GLP-1 receptor agonists, the class of drugs behind brand names like Ozempic, Wegovy, Mounjaro, and Zepbound, to a serious eye condition that can cause permanent vision loss. The condition has a long medical name: non-arteritic anterior ischemic optic neuropathy, or NAION. Eye specialists sometimes describe it as a stroke of the optic nerve. And once the damage is done, there is currently no treatment that can undo it.

This article is not written to frighten you or push you off your medication without talking to your physician. Millions of people are benefiting from these drugs every day. But the question health experts are now asking out loud is this: when a rare side effect starts appearing in large numbers of people, does it stay rare?

What Are GLP-1 Drugs and Why Are So Many People Taking Them?

GLP-1 stands for glucagon-like peptide-1. These drugs mimic a hormone your gut naturally releases after eating. They slow digestion, reduce hunger, and help control blood sugar. Originally developed for type 2 diabetes, they became household names when studies showed they could also produce significant weight loss.

The popularity of these drugs has been extraordinary. Roughly 15 million people in the United States are currently taking GLP-1 medications, and that number keeps climbing. Many of these users do not have diabetes at all. They are taking the drug specifically to lose weight, often without a full picture of what the long-term risks might look like.

The Eye Condition No One Was Expecting

NAION occurs when blood flow to the front portion of the optic nerve is cut off or severely reduced. The optic nerve is the cable that carries visual signals from your eye to your brain. When that nerve loses its blood supply, even briefly, it can suffer damage that leads to permanent partial or total vision loss in that eye. Health authorities, including the World Health Organization, confirm that this vision loss is usually permanent.

The condition is not brand new. It was already known to affect adults over 50, people with high blood pressure, and people with diabetes. What caught researchers off guard was a cluster of cases appearing in people who had recently started taking semaglutide-based medications.

The alarm was first raised in 2024, when physicians at Massachusetts Eye and Ear, a Harvard Medical School-affiliated hospital, published findings in the journal JAMA Ophthalmology. Their retrospective study of more than 16,000 neuro-ophthalmic patients found that people with type 2 diabetes or obesity who were taking semaglutide had a significantly higher rate of NAION compared to those taking other medications. Among diabetes patients in the study, semaglutide users showed a hazard ratio of 4.28, meaning the risk of developing NAION was more than four times higher than in comparable patients on other glucose-lowering drugs.

A separate Danish and Norwegian study that same year, drawing on data from more than 424,000 patients with type 2 diabetes, found that once-weekly semaglutide use more than doubled the five-year risk of NAION compared to patients taking other diabetes medications.

A Small Percentage Times Millions of People

Here is where the math matters. NAION is classified as “very rare,” meaning it may affect up to 1 in 10,000 people. The European Medicines Agency, which regulates drugs across 27 countries, formally added this classification in June 2025, recommending that product information for Ozempic, Wegovy, and Rybelsus be updated to include NAION as a side effect. The World Health Organization issued its own safety alert shortly after.

But consider what “very rare” actually means when tens of millions of people are taking a drug. If even 1 in 10,000 semaglutide users develops NAION, and 15 million Americans are using GLP-1 medications, that translates to roughly 1,500 potential cases in the United States alone. And that figure is based on the most conservative estimate.

The American Optometric Association’s clinical guidance report put it bluntly: “There is a low risk of serious ocular side effects. But a low risk of a big number is a big risk.”

The University at Buffalo researchers who published a related case series in JAMA Ophthalmology noted something else that raised eyebrows. NAION almost always strikes one eye at a time. But some patients on GLP-1 drugs were presenting with the condition in both eyes simultaneously, which is considered atypical and potentially more alarming.

The Research Is Still Sorting Itself Out

To be fair, the picture is not entirely clear-cut. A large February 2025 retrospective study that pooled data from 37 million diabetes patients across 14 international databases found that semaglutide users showed about 14 to 15 NAION cases per 100,000 patients annually, and when compared to other GLP-1 drugs, the risk was not significantly different. This suggests the vision risk may apply to the entire class of GLP-1 medications, not just semaglutide specifically.

A separate large cohort study published in JAMA Network Open, covering 185,000 individuals on GLP-1 drugs, found a slightly higher risk of developing diabetic retinopathy, but a similar rate of NAION compared to those on other treatments. And two studies presented at the American Academy of Ophthalmology’s 2025 annual meeting offered conflicting signals: one tied GLP-1 drugs to increased NAION risk and diabetic retinopathy risk, while another suggested the drugs might actually protect against dry age-related macular degeneration.

Scientists are careful to note that none of the current evidence proves that GLP-1 drugs cause NAION. What exists is a statistically significant association that has now been observed across multiple studies, multiple countries, and multiple drug databases. That is enough to prompt regulatory bodies to act and researchers to dig deeper.

Who May Be at Highest Risk?

Physicians are paying special attention to patients who already have underlying vascular risk factors. High blood pressure, high cholesterol, diabetes, a history of cardiovascular disease, and a structural eye condition called a small optic disc are all considered risk factors for NAION independent of GLP-1 use. When these pre-existing vulnerabilities are combined with a medication that may affect blood flow to the optic nerve, the potential for harm may be higher.

The symptoms to watch for are specific and sudden: vision loss in one eye that seems to come on without warning, often noticed upon waking. There may be a dark or blurry area in part of your field of vision, or a sense that something has been “wiped away” in one corner of sight. There is typically no pain, which is part of why people sometimes wait before seeking care. Any of these symptoms should be treated as a medical emergency.

Where Things Stand Right Now

As of June 2026, the European Medicines Agency has updated its labeling requirements for semaglutide to include NAION. The World Health Organization has issued a formal safety alert. And a multidistrict litigation involving GLP-1 vision loss lawsuits was consolidated in the Eastern District of Pennsylvania in December 2025. Legal analysts report that over 1,800 lawsuits had been filed by mid-2025, with more expected as scientific review continues.

The U.S. Food and Drug Administration has not yet added a NAION warning to American drug labels for semaglutide. Novo Nordisk, which manufactures Ozempic and Wegovy, has not yet updated its U.S. prescribing information to reflect the risk. Public health advocates and some legal experts have called for a black box warning, the FDA’s highest-level alert.

The American Academy of Ophthalmology and the North American Neuro-Ophthalmology Society have both weighed in, stating that they do not recommend that all semaglutide users stop their medication immediately if they develop NAION, since the benefits of the drug may still outweigh individual risks, depending on the patient’s overall health. But both organizations agree that sudden vision changes of any kind require immediate medical evaluation.

What This Means for You

If you are currently taking a GLP-1 medication for weight loss or diabetes management, here are the most important things to keep in mind.

1. Do not stop your medication without talking to your physician. For many people, the health benefits of these drugs are substantial, and an abrupt stop can create its own risks.
2. Tell your physician if you have existing eye problems, high blood pressure, or a history of cardiovascular disease. These factors may influence how closely you should be monitored.
3. Take sudden vision changes seriously. If you wake up one morning and something looks wrong with one eye, that is not something to wait out. Call your physician or go to an emergency room. Time may matter.
4. Ask questions. Ask your physician whether NAION has been discussed in your care plan. Ask whether your specific risk factors warrant more frequent eye exams. You have the right to that conversation

The Larger Question

GLP-1 medications have been genuinely life-changing for many people. They have helped reduce the burden of obesity, lower cardiovascular risk, and control blood sugar in ways that were difficult to achieve before. None of that is in dispute.

But when a drug reaches the scale of tens of millions of users, even rare side effects become a public health question. A risk that affects fewer than 1 in 10,000 people in a clinical trial still produces thousands of real individuals with real and permanent vision loss when multiplied across the population taking these drugs. Those individuals deserve answers, updated labels, and the chance to make informed decisions before the lights go out.

Research is ongoing. Regulatory conversations are happening. In the meantime, staying informed, staying in communication with your physician, and taking any sudden change in vision seriously are the most important steps you can take.

The post The Weight-Loss Drug Nobody Warned You About: When the Scale Goes Down, and Your Sight Goes With It appeared first on Medika Life.

Bringing Conversational AI to Scientific Research

The announcement arrives as artificial intelligence continues to reshape nearly every corner of the health sector. Much of the attention has focused on applications designed for consumers seeking information or clinicians seeking support in managing increasingly complex workloads. Briya is directing its attention to a different priority audience: medical researchers in academia, hospitals and life science companies.

The decision reflects a recognition that scientific inquiry often remains constrained by barriers that have little to do with science itself. Researchers routinely navigate fragmented data sources, technical requirements, analytical platforms and resource limitations before they can begin testing a hypothesis or exploring an observation.

The Next Step in Briya’s Evolution

Briya is executing on the established understanding that artificial intelligence can help reduce those barriers.

Researchers using AIRE will be able to explore public health information, including data from the U.S. Centers for Disease Control and Prevention, through a browser-based conversational interface. Rather than writing code, users can ask questions in natural language, refine their inquiry through dialogue and review the analytical pathway used to produce results.

“The last few years proved that AI can generate answers,” Briya co-founder and CEO David Lazerson told Medika Life. “The next challenge is making AI capable of generating trustworthy science. That requires a fundamental shift from general-purpose AI systems to research environments built around transparency, epidemiological methodology and scientific accountability.”

The announcement represents the latest step in Briya’s evolution. Founded in 2020 by Lazerson and Chief Technology Officer Guy Tish, the company initially centered efforts on helping organizations connect fragmented health data while maintaining privacy protections, governance requirements and institutional control over sensitive information.

Medical records rarely exist in a single location. Information is often distributed across electronic medical records, laboratory systems, imaging platforms, physician notes and institutional databases. Briya developed a federated approach that allows information to remain within source organizations while supporting approved research across participating data environments.

AIRE expands that mission from data access to data exploration.

The platform is designed to support cohort construction, endpoint validation, treatment pathway analysis, chart review and the exploration of structured and unstructured clinical information. Researchers interact with the platform through conversation rather than code, allowing them to start with a scientific question rather than a technical workflow.

The strategy mirrors an approach that has proven successful in other areas of artificial intelligence. Consumer platforms such as ChatGPT and Perplexity accelerated adoption by allowing users to experience the value of AI before deciding whether additional capabilities justified a subscription.

Reducing the Distance Between Questions and Answers

Briya is applying a similar philosophy to research. Many health technology companies continue to pursue adoption through enterprise purchasing processes, institutional pilots and lengthy implementation cycles. The Briya approach places the researcher at the center of the experience and allows investigators to determine the platform’s value through direct, frequent use.

The company believes that approach may be particularly meaningful for researchers working outside large academic medical centers and major pharmaceutical companies. Those institutions often have access to dedicated data science teams and sophisticated analytical resources. Smaller universities, physician-scientists, public health investigators and community-based researchers may not.

The absence of resources does not diminish the importance of the questions they seek to answer. In fact, as many attending HLTH EU head from Amsterdam to BIO International in San Diego, many of the biggest life-changing advances start in smaller research settings.

Giving Researchers a Seat at the Table

A physician observing an unusual treatment response, a public health researcher investigating a local health pattern, or an early-career investigator evaluating a new hypothesis all face the same challenge: transforming observation into evidence. That process frequently requires technical expertise and infrastructure that are not universally available.

Reducing those barriers could expand participation in research and potentially broaden the range of questions being explored. Accessibility alone, however, is not enough.

Scientific inquiry requires transparency, reproducibility and methodological rigor. Researchers must understand how conclusions are reached, what assumptions influence an analysis and where potential bias may exist.

A Move from Observation to Evidence

Recognizing those requirements, Briya recently appointed internationally recognized epidemiologist Professor Jonathan Samet, MD, MS, as Chief Epidemiologist. Dr. Samet is Professor of Epidemiology and Occupational and Environmental Health, and the former Dean of the Colorado School of Public Health.

“Scientific rigor and accountability cannot be layered onto AI after the fact,” Dr. Samet told Medika Life. “If these technologies are going to play a meaningful role in healthcare research, transparency, reproducibility and epidemiological methodology must be built directly into the system itself.”

Samet added that researchers need to understand more than an AI-generated conclusion: “Researchers need to understand not only what an AI system concludes, but how it reached those conclusions and what risks may exist along the way.”

His appointment reflects a broader challenge facing artificial intelligence in research environments. While generative AI systems can produce clear and persuasive responses, researchers and institutions must be able to evaluate the methods, assumptions and analytical pathways behind those outputs.

Trust, governance and cybersecurity have become as important as speed and convenience. Health information remains among the most sensitive categories of personal data. Institutions considering AI-enabled research environments must evaluate privacy protections, security controls and governance requirements alongside scientific capabilities.

Briya says its architecture is designed to allow data to remain within source organizations while supporting anonymization, compliance controls and auditable pathways for approved analysis.

Briya’s decision to open access to AIRE arrives at a time when researchers are under increasing pressure to produce meaningful scientific output while navigating growing volumes of health information. The platform’s no-cost entry point reflects a broader shift occurring across technology, where organizations increasingly recognize that adoption begins with customer experience. By allowing researchers to engage directly with data through a conversational interface, Briya is reducing barriers that have traditionally separated scientific questions from scientific exploration and adoption.

The announcement broadens the conversation surrounding artificial intelligence in health. Much of the industry’s attention has focused on consumer and clinical applications. Briya is directing attention to another critical constituency whose work influences every future therapy, diagnostic and public health intervention.

As HLTH Europe begins, the company is making the case that empowering researchers may represent one of the most consequential applications of artificial intelligence in health. If successful, the approach could help accelerate discovery, expand participation in research and provide investigators with a direct path from observation to evidence to implementation.

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But today’s advanced digital machines differ greatly from early simple computers. The digital world aims not only to copy human memory but also to implement the structure of neurons and the complex mechanisms of human neurophysiology so that machines do not have to receive data from outside every moment and can hold the information needed to perform a task in an instant.

So far, the design of advanced digital machines seems to have worked well. Where is the problem? Why, as the digital industry advances, does the human–machine interaction still break down? Why does AI remain distant from the real world of human users, when these users—clients, patients, and service recipients—must trust the technology, share information with it, and receive information from it?

As a psychologist in the learning sciences, I believe the problem is the digital industry’s lack of attention to “human deep learning.”

Where does human deep learning come from?

Until now, attempts have focused on defining deep learning for machines, and designers claim that relying on neuroscience finishes the job. But if neuroscience were sufficient, we would see fewer challenges today. The problem is that data scientists often forget that the ultimate goal is the human being—and humans are the most complex creatures. Neuroscience is only a small part of the knowledge about the mind and human development. To design human-like machines (for example, in medicine, therapy, or rehabilitation), we must explore a much broader and more diverse range of dimensions of user learning.

I call this “human deep learning.”

Although the term “deep learning” may not appear explicitly in some learning and psychology literature, its ideas are present throughout rich scholarship in the learning sciences. Cognitivist approaches in learning science—which view learning as meaningful, durable understanding or as deep change in thinking—are aligned with deep learning. These approaches argue that when a human deeply learns a belief or concept, the change endures and transfers to different but similar situations. This kind of learning is purposeful and therefore shows up in learners’ behavior and performance.

Cognitive and learning theories that speak to the idea of deep learning include Gestalt theory, Piaget, Vygotsky, Bandura (cognitive-behavioral approaches), and others.

What should we do?

If we want to design digital machines’ deep learning with these perspectives in mind, we must focus much more on behavioral sciences and learning psychology. Then the problems of mutual understanding and interaction between machines and humans will become more manageable.

As noted earlier, AI layers have been built heavily on data science, and designers claim to mimic neuroscience, but human sciences are not limited to neuroscience. If interaction between humans and machines is to occur, the processes of meaningful human learning—seen through learning science—must be discovered and used to guide machine design.

To what extent have designers taken steps in this direction?

Conclusion: Redefining AI layers

It is time to reconsider the layers of artificial intelligence. In common models, AI foundations rest mainly on data, algorithms, machine learning, neural networks, and deep learning. But if the ultimate purpose of this technology is to serve and effectively interact with humans, we must add another foundational layer: the learning sciences.

In two recent papers I wrote for the HTLH Europe 2026 conference and published in MedikaLife , I addressed the importance of the learning sciences. The first, “Human-Centered AI in Digital Health: Why Learning Sciences Matter,” discussed why learning sciences matter in digital health, a key application area for AI. The second, “Operationalizing Learning Sciences for Human-Centered AI in Digital Health,” explained some practical principles for applying learning sciences in digital health.

In this article I tried to highlight the central connection between learning sciences and AI—what I call “deep learning.” For that reason I reshaped the common pyramids that describe AI layers according to my perspective and the arguments I presented in my papers, and I proposed a new pyramid.

Learning sciences are not just an academic field; they are the foundation for understanding how humans learn, decide, change behavior, and grow cognitively. At the core of this foundation are cognitive psychology, behavioral sciences, motivation, social learning, self-regulation, and meaningful learning.

This set is what I call “human deep learning.”

In this framework, the future evolution of AI (and all AI-linked domains such as digital health) should be modeled by a pyramid in which learning sciences and human deep learning are not a side layer but the foundation of the whole structure. If AI is to work effectively in medicine, education, mental health, and other human-centered fields, it cannot rely only on data and inspiration from neuroscience. The next generation of AI must learn that humans and their learning processes are central and foundational.

The next generation of AI must learn how humans learn.

References

Ferdosipour, A. (2026). How can instruction and learning lead to deep learning in the learner? The answer from Gestalt cognitive psychologists. The Learning Guild (Accepted/In Press, July 2026 publication).

Ferdosipour, A. (2026). Why AI needs Vygotsky: The case for AI-based intentional friction. The Learning Guild.

Ferdosipour, A. (2026). Human-centered AI in digital health: Why learning sciences matter. Medika Life. https://medika.life/human-centered-ai-in-digital-health-why-learning-sciences-matter/

Ferdosipour, A. (2026). Operationalizing learning sciences for human-centered AI in digital health. Medika Life. https://medika.life/operationalizing-learning-sciences-for-human-centered-ai-in-digital-health/

Ferdosipour, A. (2026). Why biological learning demands the friction we seek to delete? Medika Life.

Ferdosipour, A. (2026). The shift from pure modernity to human-centered modernity. Medika Life.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Piaget, J. (1950). The psychology of the child. Basic Books.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Bandura, A. (1989). Social cognitive theory. Annual Review of Psychology, 40, 1–25.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning: A textbook. MIT Press.

The post Machine Deep Learning or Deep Learning of Humans? Which is Correct: “Machine Deep Learning” or “Deep Learning of Humans”? appeared first on Medika Life.

The Centers for Medicare & Medicaid Services released the rules on June 1. That deadline was set last year in the GOP tax-and-spending law known as the One Big Beautiful Bill Act, which established a work requirement for certain people enrolled in Medicaid, the state-federal health insurance program for people with low incomes or disabilities.

Medicaid agencies are scrambling to rework IT systems and make sure they have staff to effectively enforce the rules, while also keeping enrollees from losing coverage for administrative reasons, such as difficulty navigating state eligibility portals.

The newly announced regulations offer a clearer picture of what roughly 18.5 million Medicaid enrollees will have to do to prove they qualify for benefits.

Jim Torres, who helps people enroll in health coverage at the Samuel U. Rodgers Health Center in Kansas City, Missouri, said a “very small percentage” of his clients have heard of the changes coming to Medicaid.

“These folks have very busy lives. They’re doing the best they can to get by,” he said. “It’s just not a top-of-mind thing for most of them.”

Health policy researchers and consumer advocates said enrollees should keep a few things in mind as the Jan. 1, 2027, rollout approaches in most states.

1. The work rules won’t apply to everyone.

The new rules will apply to people covered through what’s known as Medicaid expansion. Since 2014, more than 40 states and the District of Columbia have decided to allow more people into their Medicaid programs, generally low-income adults without dependents. Georgia and Wisconsin offer coverage to some people in this group, so they’ll be subject to the rules.

Children and pregnant people, as well as individuals with disabilities who receive Social Security payments — all groups that already qualify for Medicaid — won’t be subject to the rules. Nor will people determined to be “medically frail,” or too sick to work.

People subject to the work rules are “crowding out” people in the Medicaid program who are “truly in need,” CMS Administrator Mehmet Oz claimed during a June 1 press call. “Work requirements are going to turn this around, we hope.”

The rules are set to take effect in most places in January. Nebraska started enforcing them in May. Montana plans to start in July but won’t kick people off until October. Arkansas will do a “soft” launch in July — it will start enforcing the rules but with no penalties until next year.

2. States will take your word that you’re too sick to work. For now.

Federal officials have stressed that states should make the process of reporting hours and requesting exemptions as simple as possible for Medicaid enrollees by creating automated systems and using existing data sources, such as unemployment and education records.

If states cannot determine you’re performing 80 hours of qualifying activities a month using those data sources, you may be allowed to “self-attest” to that in 2027, health policy researchers said.

People will also be allowed to “self-attest” that they are too sick to work in 2027, and do so one time in 2028. Then states will start asking for proof, if they can’t find it through available data.

But after the initial rollout, the burden of proof is likely to still fall on many enrollees, said researchers and consumer advocates.

People may need to dig up pay stubs, medical records, and doctors’ notes and submit them for state review, said Morgan Henderson, who has studied Medicaid work programs in Georgia and Arkansas at The Hilltop Institute, a research center at the University of Maryland-Baltimore County.

“The higher this manual reporting burden, the less people are going to do it,” he said. “That means that we’re going to see coverage drop-offs.”

3. The rules are tougher than expected for people too sick to work.

One of CMS’ primary goals has been to “protect vulnerable populations” through “strong exemptions to make sure people who can’t reasonably be expected to work are not subject to the requirements,” Dan Brillman, a deputy administrator at the agency, said during the June 1 press call.

Consumer and patient advocates, however, said the final rules’ exemptions are more restrictive than expected. Enrollees will eventually have to provide documentation, such as a statement from a medical professional, to prove that a health condition keeps them from working. And each individual state will have to determine the severity of beneficiaries’ medical conditions.

“Someone could be medically frail in Nebraska but not medically frail in Delaware,” said Carolyn Sheridan, associate director of state policy for the National Organization for Rare Disorders, which lobbies for patients with rare diseases. She said her group had hoped the rules would offer a standardized definition of who counted as medically frail and not leave the decision up to states.

Trump administration officials have publicly crusaded against fraud in government health programs, such as Medicaid, and states could face financial penalties for incorrectly granting people exemptions from the work rules, said Jennifer Tolbert, who researches Medicaid at KFF, a health information nonprofit that includes KFF Health News.

“States may be more cautious,” she said. “That will likely lead to people losing coverage who may still be eligible.”

4. Only certain qualifying activities count.

Enrollees can satisfy the rules by working 80 hours a month. They can also be enrolled in college courses, volunteer through a community organization, or do “in-kind” work that doesn’t result in pay.

The rules set out, in detail, how many academic credit hours translate to 80 hours a month — students need to be enrolled in six credit hours per semester to meet the “half-time” requirement. An unpaid internship can count toward the 80 hours.

People can also prove they’re volunteering with “a document from a community service organization.”

Consumer advocates say it might be hard for people to obtain proof they’re performing these kinds of informal activities. But supporters of the rules say volunteerism can already be tracked.

“If you run into trouble with the law and the judge says, ‘Hey, you need some volunteering and community service to serve your time,’ there are already ways that we verify that,” said Niklas Kleinworth, who works on state health policy for the conservative Paragon Institute.

5. You have time to prepare.

Make sure your state Medicaid agency has your current mailing address and keep your eye on your mailbox, said researchers and consumer advocates. State Medicaid agencies must inform you in two ways if you’ll be subject to the rules — by either regular mail or email, and by one other form of communication, such as a text or phone call or by posting a notice online.

“The important stuff comes by mail,” Henderson said.

And check in with your state Medicaid agency, said researchers and advocates. Some states, including Arkansas, California, and Wisconsin, have already posted information about the work rules on their websites. If you can’t find what you’re looking for there, visit or call a local office. A caseworker should be able to tell you whether you’ll be subject to the rules.

“Get ahead of this,” said Joan Alker, who is executive director of the Georgetown University Center for Children and Families and studies Medicaid. “So that you don’t end up going to the pharmacy one day and they say, ‘Oh, you’re not insured anymore’ when you’re trying to get your prescriptions refilled.”

KFF Health News correspondent Samantha Liss and senior correspondent Rachana Pradhan contributed to this report.

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Loneliness, it turns out, isn’t just an emotion. It’s a biological signal, as powerful and urgent as hunger or thirst. When you’re lonely, your brain doesn’t just feel sad. It sounds an alarm. Your body responds. And if that alarm keeps ringing, day after day, real physical damage begins.

Your Brain Treats Loneliness Like Starvation

Here’s something researchers at MIT discovered when they had people sit alone in a room for ten hours: afterward, when those isolated individuals looked at pictures of people laughing and connecting, the same part of their brain lit up that activates in people who are starving and looking at food. That’s not a metaphor. The craving for company and the craving for food share the same neural real estate.

Kay Tye, a neuroscientist at the Salk Institute for Biological Studies in California, has spent years mapping what she calls “social homeostasis” in the brain. Homeostasis is the fancy word for the way your body stays balanced. Your temperature stays near 98.6 degrees. Your blood sugar stays in a range. Tye’s research suggests that your need for human connection operates in the same way. Your brain has a set point. Stray too far from it, and systems start firing to bring you back.

In 2025, a paper published in Biological Psychiatry by Tye and colleagues formally introduced “social homeostasis” as a new way to think about mental health. The authors argue that chronic loneliness or overcrowding can shift the brain’s set point, leading to the kind of nervous system imbalance seen in many psychiatric conditions.

Deep Inside the Brain, a Social Thermostat

Catherine Dulac, a neuroscientist at Harvard University, wanted to know exactly where in the brain this social regulation lives. She looked to the hypothalamus, the ancient region buried deep in our skulls that controls hunger, thirst, and sleep. It turned out to be the right place to look.

In 2025, her team published findings from experiments on mice that had been separated from their companions for 5 days. They found two distinct clusters of neurons in the hypothalamus. One cluster fired when the animals were alone. The other fired when they were reunited. More telling: the longer the animals had been isolated, the more intensely they sought contact once reunion was possible. Greater deprivation, greater need. Just like thirst.

When researchers artificially activated the “separation” neurons, the mice actively avoided whatever chamber triggered the signal. Being alone felt bad, in a physical, measurable way. When they activated the “reunion” neurons, which connect to the brain’s dopamine reward system, the mice sought out that feeling. Connection felt good. Not just emotionally. Chemically.

These deep brain structures look nearly identical in mice and humans. We share this wiring because it’s ancient. The need to belong is not a modern luxury. It’s a survival code, written into the oldest parts of who we are.

Touch Matters More Than You Think

In Dulac’s experiments, vision didn’t count for much. Neither did sound or smell. Mice separated by a screen that still let them hear and smell each other reacted as if they were fully alone. The only sense that truly registered “I’m not alone” was touch. The physical presence of another body against their own.

Ishmail Abdus-Saboor, a neurobiologist at Columbia University, studies the specific nerve pathways dedicated to social touch in human skin. Our bodies actually have neurons in hairy skin that respond specifically to slow, gentle stroking, the kind a friend or family member might offer. These aren’t generic touch receptors. They’re wired for connection. A hug or a hand on the shoulder isn’t just a nice gesture. It’s information your nervous system uses to update its social score.

This is why phone calls help but don’t completely fill the gap. Why video chats feel better than nothing, but still leave something missing. Your brain needs data that only physical proximity can provide.

When Loneliness Goes Untreated, Your Body Pays

Social disconnection isn’t just hard on the heart emotionally. It’s hard on the actual heart. Research published in Cureus in 2025 reviewed data spanning decades and found that loneliness nearly doubles the risk of stroke and recurrent coronary artery disease, driven largely by increased inflammatory responses in the body.

The American Heart Association has stated that social isolation and loneliness raise the risk of heart attack, stroke, or death from either condition by about 30 percent. And a 2025 narrative review published in the journal Stress mapped the full internal chain of events: loneliness activates the body’s stress response system, raises cortisol levels, increases inflammatory proteins in the blood, changes how the amygdala reacts to social threat, and contributes to cardiometabolic risk markers.

Research published in Frontiers in Human Neuroscience in 2026 linked chronic loneliness to reduced gray matter in brain regions involved in memory and emotional regulation, including the hippocampus. It also found that loneliness is a significant risk factor for accelerated cognitive decline and dementia.

Put simply: isolation doesn’t just make us miserable. It changes our brain structure, disrupts our hormones, inflames our blood vessels, and shortens our lives.

What This Means for How We Live

Knowing that connection is a biological need rather than a preference changes the conversation. You’re not weak if you feel lonely. You’re not clingy if you crave company. You’re responding to an ancient alarm system that has kept our species alive for hundreds of thousands of years.

The research also offers a practical insight. Because touch plays such a central role, the quality of our physical presence with others matters enormously. Abdus-Saboor says he’s intentional about physical contact with his family every single day. Not grand gestures. Just a hug before the kids leave for school. A hand on a shoulder. A back rub. These aren’t small things. They’re medicine.

Tye adds another useful idea: building a variety of social settings into your life. Spending time alone, in small groups, and occasionally in larger groups can help your social thermostat stay flexible and resilient. The goal isn’t constant togetherness. It’s a healthy range.

It’s also worth noting that the damage from loneliness isn’t inevitable. Research on loneliness as a health issue consistently points to the same takeaway: these effects are modifiable. Community programs, social prescribing in healthcare, nature-based group activities, and intentional outreach to isolated neighbors all show measurable results.

The Bottom Line

If you feel lonely, don’t brush it off as a mood that’ll pass. Your brain is signaling something real. Your body is already responding. The good news is that connection, real physical presence with people who matter to you, works as powerfully in the other direction. It turns the alarm off. It restores the balance. It’s not a luxury. It’s what your biology has been asking for all along.

Science is saying what our hearts have always known: we need each other. Now we know exactly why.

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Diabetes

40 million Americans have diabetes mellitus, or 12% of the population. Of these 38 million have type 2 diabetes (previously known as “adult onset”) and 2 million have type 1 (previously known as “juvenile diabetes.) 29 million have been diagnosed, yet 11 million are unaware that they have the disease. Our focus is type 2 diabetes.

Diabetes type 2 incidence increases with age, such that nearly 30% of those over 65 have diabetes, compared to ages of 40 to 59 with a 12% incidence, and 4% for those under 40.

Of great concern is the rising incidence over time. In 2000, a total of 8% of Americans had diabetes. This has risen to about 12% or a 50% increase in just two decades. Some would term this an epidemic. Of further concern, many young people are now developing diabetes.

Diabetes substantially increases the risk of multiple diseases. Among them are blindness, kidney failure (the most common reason for a kidney transplant is diabetes damage), cardiovascular disease, including heart attacks and strokes, loss of sensation in the feet and lower legs (neuropathy and paresthesia), reduced vascular supply to the lower legs and the feet, often resulting in amputation of toes, feet, or legs, and Alzheimer’s disease.

Diabetes and its associated diseases combined cost America $640 billion per year, or 25% of all healthcare spending.

The major risk factors for type two diabetes include being substantially overweight, having a family history, being inactive or sedentary, and being over the age of 45.

You can’t change your family history. But your genes need not be your destiny. The basics of prevention include losing weight, increasing activity, eating healthy foods, and substantially reducing the intake of ultra-processed foods.

Diabetes and Alzheimer’s disease

Type 2 diabetes increases the risk of Alzheimer’s disease by 50 to 65%. It’s a shared risk factor with increased blood pressure and increased cholesterol, all leading to brain cell inflammation.

The increased risk of diabetes correlates with insulin resistance not only throughout the body but also in the brain. Some scientists think that brain insulin resistance is separate from the resistance in other parts of the body, and as a result, use the term “type 3 diabetes” as a synonym for Alzheimer’s disease. The brain cells are in effect “starved” for glucose, which is the neurons’ principal source of energy, despite plenty of glucose circulating in the bloodstream.

Insulin resistance of brain cells is associated with increased oxidative stress and neuroinflammation, which in turn leads to neurodegeneration.

Bottom line — reducing the possibility of developing diabetes markedly reduces your chances of developing Alzheimer’s disease. Diabetes begins with prediabetes, so the place to start your preventive work is there.

Prediabetes

115 million Americans have prediabetes. 81% do not know it. 18% of teens have prediabetes, and this number is on the rise.

The typical person with prediabetes is overweight, has excess belly fat, and is sedentary. They tend to eat a diet high in sugar and foods that are quickly digested into sugar, as well as ultra-processed foods. Many will also have a family history of diabetes.

Those with prediabetes have a 5–10% chance of progressing to diabetes each year, which accumulates so that in 3–5 years, 15–30% will have developed diabetes. And, if followed for enough years, up to 70% will progress.

Prediabetes is diagnosed by measuring your average blood glucose over the course of 2 to 3 months. This is done with a test called A1c, a blood test that can be drawn at any time of day. If the A1c is between 5.7% and 6.4%, that is diagnostic of prediabetes. If it’s 6.5% or higher, that’s diagnostic of diabetes. As noted, most people with prediabetes don’t know it. However, the A1c test is simple and inexpensive. If you have any of the risk factors for prediabetes, it’s well worth your while to have the test. Your doctor may order it at your annual evaluation. Ask to have it done and then ask for the result.

Knowing you have prediabetes is essential because the progression to diabetes can be reversed with lifestyle changes. Yes, these can be challenging, but they are well worth the time and effort so that you do not progress to diabetes and all of its downstream complications, including dementia.

The other reason to reverse prediabetes is that it is not a benign condition. It is silent but causes trouble over time. Prediabetes leads to slow but long-term damage to blood vessels, the heart, and the kidneys. Prediabetes can be part of the metabolic syndrome — a combination of any three of elevated blood sugar, high blood pressure, high LDL cholesterol and low HDL cholesterol, elevated triglycerides, and excess belly fat. These conditions in combination lead to diabetes, heart disease, stroke, fatty liver disease, and cognitive decline.

Although usually not measured, the underlying problem is the early stages of insulin resistance when the pancreas can still compensate by producing excessive insulin to overcome the resistance. Once it can no longer do that, blood glucose remains high, and you now have diabetes.

Reversing prediabetes

The good news is that you can reverse prediabetes back to normal. The steps are straightforward and usually do not require medication. Since prediabetes develops because of being overweight, being sedentary, and eating too much sugar and foods that readily convert to sugar, the steps are straightforward.

Check your waist-to-height ratio. Your waist should be less than 50% of your height. Details here.

Losing just 5 to 7% of body weight will reduce the risk of progressing to diabetes by 50%. For a 200-pound person, this is only 10–14 pounds. The second step is to get about 150 minutes of aerobic exercise each week. A brisk 30-minute walk five days a week will suffice. Add resistance exercises 2 to 3 times per week.

The third step is to eat a low-glycemic (meaning food that does not digest to sugar rapidly and thereby leads to rapid increases in blood sugar — think candy, ice cream, cakes), micronutrient-dense diet that includes increased fiber intake, non-starchy vegetables (for example, dark green veggies), whole grains (whole wheat, brown rice), legumes (beans and lentils), lean meats, fish, eggs, nuts, and avocados.

It is very important to reduce sugary drinks and high-sugar foods, such as pastries, pies, and ice cream. Eating only whole grains means no products made from white flour, such as white bread, most prepared cereals, pastries, and donuts.

Avoid ultra-processed foods, as they contain high levels of sugar, salt, and white flour. Ultra-processed foods themselves are linked to dementia. It is also important to get adequate sleep and to reduce chronic stress.

It’s a good idea to monitor your progress with continuous glucose monitoring. It will show you whether certain foods cause spikes in blood sugar and how exercise affects it. These monitors are now available at most pharmacies.

These lifestyle changes can reverse prediabetes within a few months.

You want to get your A1c level down, not just to the upper limit of normal at 5.6, but lower still, because the cutoff from normal to prediabetes, although highly useful, is still a static number. You want to be well under the top limit of “normal.”

Some people have tried but cannot lose weight or can’t keep it down after losing. They often benefit from using a GLP1 drug such as Wegovy (semaglutide) and Zepbound (tirzepatide). For many people, these have proven highly valuable. A word of caution. Weight loss usually includes both fat and muscle, so you need to do resistance exercises to counteract muscle loss. Once started, many people need to continue indefinitely, otherwise they regain weight, fat, but not muscle.

It is to your definite advantage to learn if you have prediabetes and then to do what is needed to reverse it. Habits can be difficult to modify, but the benefits are so great that the time and effort are well worth it, including a major risk reduction in dementia. And remember, it is never too late to get started.

With thanks to retired long-time expert diabetes educator Charlene Freeman, RN CDE CPT

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